Learn to train and evaluate a generator and discriminator together. Build a small image-generation experiment, inspect output diversity, and diagnose training i
Last reviewed: 2026-10-02
A generative adversarial network trains two models together. The generator produces samples, while the discriminator learns to distinguish generated samples from training examples. The generator receives a learning signal through that adversarial objective. Understanding the interaction between the two models is central to training and evaluating a GAN.
Start with a small, inspectable experiment. TensorFlow's DCGAN tutorial uses handwritten digits to demonstrate image preparation, separate losses and optimizers, a training loop, and saved checkpoints. Reproducing that workflow helps you inspect what each component does before attempting a new dataset or higher-resolution output.
GAN training can suffer from mode collapse, vanishing gradients, and failure to converge. A plausible individual image is therefore not enough evidence of a successful model. Inspect a fixed set of generated samples over time, compare variety across samples, and document failure cases alongside training curves.
For fashion exploration, a proposed portfolio exercise is to generate small textile swatches from a permitted reference collection. Keep the task narrow and ask a designer to review repetition, diversity, and resemblance to the references. Treat this as a learning experiment, not evidence that generated material is original, manufacturable, or ready to publish.
No. This page teaches adversarial model training. Using a Stable Diffusion checkpoint is a separate workflow with its own model documentation. Learn the architecture relevant to the model you intend to train or deploy.
Mode collapse occurs when a generator produces a limited variety of outputs instead of representing the broader data distribution. Inspect diversity across many samples; selecting a few attractive outputs can hide this failure.
Show how you prepared the data, trained both networks, saved and compared outputs, and investigated failures. Include limitations and review criteria so another builder can judge the experiment beyond a single sample image.